Fast annual daylighting simulation and high dynamic range image processing using NumPy

Abstract

Annual daylighting and point-in-time glare calculations depend on manipulating large matrices representing annual light levels and image pixels, respectively. Advances in computation speed have allowed modern green building standards like LEED, WELL, and EN 17037 to require annual daylighting calculations at a fine resolution across a building’s floorplate. As parallel ray tracing speeds become faster and cameras increase in resolution, matrix manipulation itself has emerged as a bottleneck to delivering fast simulation results. This paper describes a how vectorized computation methods can be applied to annual daylighting simulation and high dynamic range (HDR) image processing. Vectorization allows a computer to perform simple operations on large amounts of data simultaneously. We use the NumPy library in Python to vectorize multiple steps in the calculation of spatial daylight autonomy and annual renderings for glare as example climate-based daylighting metrics, achieving a speedup of two orders of magnitude. We also show that NumPy can manipulate large HDR images with speedups for individual operations up to six orders of magnitude.